import torch

from transformers import AutoModel




class        _snake_case    (    torch.nn.Module					):
					def __init__(	self:					str						,	__lowerCamelCase:					Tuple="sayef/fsner-bert-base-uncased"  )     ->					str:
												super(__SCREAMING_SNAKE_CASE						,	self  ).__init__()

												__UpperCAmelCase    :      Tuple       = AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,	return_dict=__SCREAMING_SNAKE_CASE  )
												__UpperCAmelCase    :      Tuple       = torch.nn.CosineSimilarity(3						,	1e-08  )
												__UpperCAmelCase    :      str       = torch.nn.Softmax(dim=1  )
					def  _lowerCamelCase			(	self:					Optional[int]						,	**__lowerCamelCase:					Optional[int]  )     ->					int:
												return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state
					def  _lowerCamelCase			(	self:					Any						,	__lowerCamelCase:					Optional[int]  )     ->					Optional[Any]:
												return token_embeddings.sum(2						,	keepdim=__SCREAMING_SNAKE_CASE  )
					def  _lowerCamelCase			(	self:					Optional[int]						,	__lowerCamelCase:					str						,	__lowerCamelCase:					Union[str, Any]						,	__lowerCamelCase:					str=1  )     ->					Optional[Any]:
												return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE  )  )





					def  _lowerCamelCase			(	self:					Optional[int]						,	__lowerCamelCase:					int						,	__lowerCamelCase:					Dict  )     ->					Optional[Any]:
												__UpperCAmelCase    :      Any       = W_supports["sizes"].tolist()
												__UpperCAmelCase    :      Optional[Any]       = W_supports["start_token_id"].item()
												__UpperCAmelCase    :      List[str]       = W_supports["end_token_id"].item()

												del W_supports["sizes"]
												del W_supports["start_token_id"]
												del W_supports["end_token_id"]

												__UpperCAmelCase    :      int       = self.BERT(**__SCREAMING_SNAKE_CASE  )
												__UpperCAmelCase    :      List[str]       = self.BERT(**__SCREAMING_SNAKE_CASE  )

												__UpperCAmelCase    :      Dict       = None
												__UpperCAmelCase    :      Dict       = None

												__UpperCAmelCase    :      List[Any]       = W_supports["input_ids"] == start_token_id
												__UpperCAmelCase    :      Dict       = W_supports["input_ids"] == end_token_id

												for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
																			if i == 0:
																										__UpperCAmelCase    :      Union[str, Any]       = 0
																			else:
																										__UpperCAmelCase    :      Union[str, Any]       = support_sizes[i - 1]

																			__UpperCAmelCase    :      Optional[Any]       = S[s : s + size][start_token_masks[s : s + size]]
																			__UpperCAmelCase    :      Union[str, Any]       = S[s : s + size][end_token_masks[s : s + size]]

																			__UpperCAmelCase    :      Union[str, Any]       = torch.matmul(q[i]						,	s_start.T  ).sum(1  ).softmax(0  )
																			__UpperCAmelCase    :      List[Any]       = torch.matmul(q[i]						,	s_end.T  ).sum(1  ).softmax(0  )

																			if p_starts is not None:
																										__UpperCAmelCase    :      str       = torch.vstack((p_starts, p_start)  )
																										__UpperCAmelCase    :      Optional[int]       = torch.vstack((p_ends, p_end)  )
																			else:
																										__UpperCAmelCase    :      List[str]       = p_start
																										__UpperCAmelCase    :      Optional[int]       = p_end

												return p_starts, p_ends












"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   :							List[str]          =		{}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							str          =		['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _SCREAMING_SNAKE_CASE   :							Optional[Any]          =		['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  _SCREAMING_SNAKE_CASE   :							Any          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




